T. Joclyn Sharon
Papers
1
Total Citations
4
H-Index
1
About
T. Joclyn Sharon is a biomedical engineer whose research focuses on the intersection of human movement analysis and intelligent signal processing. Her primary work centers on decoding neuromuscular signals to estimate joint kinematics, with a particular emphasis on the elbow joint. In her most-cited study, "Estimation of Elbow Joint Angle from Surface Electromyogram Signals Using ANFIS" (2019), she pioneered the application of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to transform raw surface electromyogram (sEMG) data into accurate predictions of joint angle. This contribution is critical for advancing prosthetic control, rehabilitation robotics, and human-machine interfaces, offering a non-invasive, real-time method for interpreting muscle activity. While her citation count is modest, her work represents a foundational step in merging fuzzy logic with neural networks for biomechanical estimation, a niche yet impactful area. Her research underscores a commitment to developing accessible, data-driven solutions for assistive technologies, positioning her as a promising voice in the field of biomedical signal processing and rehabilitation engineering.
Research Focus
Key Achievements
Top Papers
- 1